Namespace AiDotNet.TimeSeries
Classes
- ARIMAModel<T>
Implements an ARIMA (AutoRegressive Integrated Moving Average) model for time series forecasting.
- ARIMAXModel<T>
Implements an ARIMAX (AutoRegressive Integrated Moving Average with eXogenous variables) model for time series forecasting.
- ARMAModel<T>
Implements an ARMA (AutoRegressive Moving Average) model for time series forecasting.
- ARModel<T>
Implements an AR (AutoRegressive) model for time series forecasting.
- AutoformerModel<T>
Implements the Autoformer model for long-term time series forecasting with decomposition.
- BayesianStructuralTimeSeriesModel<T>
Implements a Bayesian Structural Time Series model for flexible time series forecasting.
- ChronosFoundationModel<T>
Implements the Chronos foundation model for zero-shot time series forecasting.
- ChronosOptions<T>
Options for Chronos foundation model.
- DeepARModel<T>
Implements DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks.
- DynamicRegressionWithARIMAErrors<T>
Implements a Dynamic Regression model with ARIMA errors for time series forecasting.
- ExponentialSmoothingModel<T>
Represents a model that implements exponential smoothing for time series forecasting.
- GARCHModel<T>
Represents a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model for time series with changing volatility.
- InformerModel<T>
Implements the Informer model for efficient long-sequence time series forecasting.
- InterventionAnalysisModel<T>
Represents a model that analyzes and forecasts time series data with interventions or structural changes.
- MAModel<T>
Implements a Moving Average (MA) model for time series forecasting.
- NBEATSBlock<T>
Represents a single block in the N-BEATS architecture.
- NBEATSModel<T>
Implements the N-BEATS (Neural Basis Expansion Analysis for Time Series) model for forecasting.
- NHiTSModel<T>
Implements N-HiTS (Neural Hierarchical Interpolation for Time Series) for efficient long-horizon forecasting.
- NeuralNetworkARIMAModel<T>
Represents a Neural Network ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting.
- ProphetModel<T, TInput, TOutput>
Represents a Prophet model for time series forecasting.
- SARIMAModel<T>
Implements a Seasonal Autoregressive Integrated Moving Average (SARIMA) model for time series forecasting.
- STLDecomposition<T>
Implements Seasonal-Trend decomposition using LOESS (STL) for time series analysis.
- SpectralAnalysisModel<T>
Implements spectral analysis for time series data, which transforms time domain signals into the frequency domain.
- StateSpaceModel<T>
Implements a State Space Model for time series analysis and forecasting.
- TBATSModel<T>
Implements the TBATS (Trigonometric, Box-Cox transform, ARMA errors, Trend, and Seasonal components) model for complex time series forecasting with multiple seasonal patterns.
- TemporalFusionTransformer<T>
Implements the Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting.
- TimeSeriesModelBase<T>
Provides a base class for all time series forecasting models in the library.
- TransferFunctionModel<T>
Implements a Transfer Function Model for time series analysis, which combines ARIMA modeling with external input variables to capture dynamic relationships between multiple time series.
- UnobservedComponentsModel<T, TInput, TOutput>
Implements an Unobserved Components Model (UCM) for time series decomposition and forecasting.
- VARMAModel<T>
Implements a Vector Autoregressive Moving Average (VARMA) model for multivariate time series forecasting.
- VectorAutoRegressionModel<T>
Implements a Vector Autoregression (VAR) model for multivariate time series forecasting.